基于自编码器的多式预测非小细胞肺癌存活率
Jacob G Ellen1, Etai Jacob2, Nikos Nikolaou2
1Institute of Health Informatics, University College London, London, UK. jellen@hms.harvard.edu.
Scientific reports
|September 22, 2023
概括
精确的非小细胞肺癌 (NSCLC) 存活预测通过整合多omics数据而得到增强. 结合长非编码RNA (lncRNA) 和临床数据,产生了最好的NSCLC预后,优于单个数据类型.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 准确预测非小细胞肺癌 (NSCLC) 患者生存率对于临床决策至关重要.
- 多omics数据为改善预后准确性提供了潜力.
研究的目的:
- 开发和评估一种多式联通数据整合方法,用于预测NSCLC患者的存活率.
- 根据分子和临床数据识别不同的NSCLC患者亚型.
主要方法:
- 用于数据压缩和集成microRNA,mRNA,DNA甲基化,lncRNA和临床数据的自动编码器.
- 通过使用癌症基因组图谱 (TCGA) 数据,比较不同模式组合和整合方法的生存预测性能.
- 分析生物差异以确定不同的生存亚型.
主要成果:
- 多模式数据整合模型在预测肺腺癌 (LUAD) 和肺状细胞癌 (LUSC) 的NSCLC存活率方面表现优于单模式模型.
- lncRNA和临床数据的结合实现了最高的预测性能 (C指数为0.69 ± 0.03的LUAD和0.62 ± 0.03的LUSC).
- 分析确定了两个不同的生存亚型,其特征是超过900个差异表达的转录.
结论:
- 整合多个omics数据类型,特别是lncRNA和临床数据,显著提高NSCLC生存预测的准确性.
- 多式联络方法可以帮助识别不同的患者子组,并可能指导个性化治疗策略.
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